About

Sofie Salama is a Professor of Molecular, Cell & Developmental Biology at UCSC. She holds a B.S. from the University of Illinois Urbana-Champaign, a Ph.D. from the University of California, Berkeley, and completed postdoctoral training at Massachusetts General Hospital Cancer Center and Harvard Medical School. Her lab uses pluripotent stem cells from various primate species, along with high throughput sequencing and phenotyping methods, to study how genome evolution influences human development and disease. Her research explores mechanisms by which retrotransposons and rapidly evolving transcriptional repressors, specifically KRAB zinc finger proteins, lead to new gene regulatory programs. She employs undifferentiated pluripotent stem cells as models of the early embryo and utilizes pluripotent stem cell-derived cerebral cortex organoids to investigate human brain development and disease. Her projects include the braingeneers initiative, a multidisciplinary collaboration between UCSC and UCSF, which aims to develop reproducible, scalable brain organoid models and cost-effective, non-invasive methods for analyzing their molecular and functional phenotypes. Recent advancements include microfluidic systems for automated organoid culturing, in-incubator imaging, and high-density multielectrode arrays for neural activity measurement. Her work also involves studying the function of human-specific genes and variants, such as NOTCH2NL genes, which are associated with neurodevelopmental disorders like autism spectrum disorders and schizophrenia. Additionally, as part of UCSC's Treehouse Pediatric Cancer Initiative, her research extends to using hindbrain and forebrain organoids to study pediatric gliomas, a form of cancer with limited treatment options. Her research aims to deepen understanding of human brain development, evolution, and disease through innovative stem cell and organoid models.

Research topics

• Computer Science
• Political Science
• Computational biology
• Oncology
• Internal medicine
• Genetics
• Medicine
• Biology

Selected publications

• Comparative analysis of RNA expression in a single institution cohort of pediatric cancer patients

  medRxiv (Cold Spring Harbor Laboratory) · 2024

  • Political Science
  • Computer Science
  • Oncology

  Abstract Importance Genomic analyses solely focused on detecting mutations do not benefit most pediatric cancer patients. Alternate genomic approaches are needed to identify additional treatment biomarkers and therapeutic targets. Objective To evaluate the performance of our Comparative Analysis of RNA Expression (CARE) pipeline in nominating druggable targets in pediatric patients with difficult-to-treat or rare cancers. Design, Setting, and Participants Our cohort study, the Comparative Analysis of RNA Expression to Improve Pediatric and Young Adult Cancer Treatment (CARE IMPACT), was conducted collaboratively by the UC Santa Cruz Treehouse Childhood Cancer Initiative and Stanford University School of Medicine. From June 4, 2018, to September 24, 2020, UCSC Treehouse obtained and processed RNA sequencing (RNA-Seq) data for 35 tumor samples from 33 children and young adults with a relapsed, refractory or rare cancer. Each tumor RNA-Seq dataset underwent CARE analysis to reveal activated cancer driver pathways and nominate treatment options. We compare the CARE pipeline to other gene expression outlier detection approaches and discuss challenges and opportunities for the clinical implementation of RNA-Seq-based gene expression outlier detection for pediatric cancer patients. Exposures Patients underwent tumor RNA-Seq analysis and standard-of-care tumor DNA profiling. UCSC Treehouse compared each patient’s tumor RNA-Seq profile with over 11,000 uniformly analyzed tumor profiles from public data repositories. These comparisons reveal candidate cancer genes and pathways that represent potential therapeutic targets. Main Outcome(s) and Measure(s) Proportion of patients for whom CARE provided useful clinical and biological information for patient care. Impact of comparator cohort choice on outlier findings. Results Of our 33 patients, 31 (94%) had CARE IMPACT findings of potential clinical significance. These findings were implemented in 5 patients, 3 of which had defined clinical benefit. We demonstrated that composition of comparator cohorts determines which outliers are detected and that large and diverse cohorts containing data from tumors similar to the patients produce the most clinically relevant outlier results. Conclusions and Relevance Comparative RNA-Seq analysis may identify additional cancer driver pathways and druggable targets in patients with rare or difficult-to-treat pediatric cancers relative to standard-of-care DNA profiling. This study highlights the clinical utility of CARE for pediatric tumors and underscores the need for further evaluation of this approach to improve patient outcomes. Key Points Questions Is tumor RNA expression information useful for the clinical care of children with difficult-to-treat or rare cancers? How does choice of comparator cohort impact RNA expression results? Findings In this cohort of 33 pediatric patients, Comparative Analysis of RNA Expression provided useful clinical information for all patients; three of five patients who received treatment derived clinical benefit. We demonstrate the impact of comparator cohort composition on RNA outlier analysis. Meaning Tumor RNA expression information reveals useful information for the clinical care of pediatric cancer patients. Choice of comparator cohort size and composition impacts gene expression outlier detection.

  PublisherOA PDFDOI

• Complete genomic and epigenetic maps of human centromeres

  Science · 2022 · 612 citations

  • Biology
  • Genetics
  • Computational biology

  Existing human genome assemblies have almost entirely excluded repetitive sequences within and near centromeres, limiting our understanding of their organization, evolution, and functions, which include facilitating proper chromosome segregation. Now, a complete, telomere-to-telomere human genome assembly (T2T-CHM13) has enabled us to comprehensively characterize pericentromeric and centromeric repeats, which constitute 6.2% of the genome (189.9 megabases). Detailed maps of these regions revealed multimegabase structural rearrangements, including in active centromeric repeat arrays. Analysis of centromere-associated sequences uncovered a strong relationship between the position of the centromere and the evolution of the surrounding DNA through layered repeat expansions. Furthermore, comparisons of chromosome X centromeres across a diverse panel of individuals illuminated high degrees of structural, epigenetic, and sequence variation in these complex and rapidly evolving regions.

  PublisherOA PDFDOI

• Nanopore sequencing and the Shasta toolkit enable efficient de novo assembly of eleven human genomes

  Nature Biotechnology · 2020 · 647 citations

  • Computational biology
  • Biology
  • Genetics

  De novo assembly of a human genome using nanopore long-read sequences has been reported, but it used more than 150,000 CPU hours and weeks of wall-clock time. To enable rapid human genome assembly, we present Shasta, a de novo long-read assembler, and polishing algorithms named MarginPolish and HELEN. Using a single PromethION nanopore sequencer and our toolkit, we assembled 11 highly contiguous human genomes de novo in 9 d. We achieved roughly 63× coverage, 42-kb read N50 values and 6.5× coverage in reads >100 kb using three flow cells per sample. Shasta produced a complete haploid human genome assembly in under 6 h on a single commercial compute node. MarginPolish and HELEN polished haploid assemblies to more than 99.9% identity (Phred quality score QV = 30) with nanopore reads alone. Addition of proximity-ligation sequencing enabled near chromosome-level scaffolds for all 11 genomes. We compare our assembly performance to existing methods for diploid, haploid and trio-binned human samples and report superior accuracy and speed.

  DOI

• Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction

  Cell · 2020 · 524 citations

  • Biology
  • Computational biology
  • Immunology
  DOI

• Sequence diversity analyses of an improved rhesus macaque genome enhance its biomedical utility

  Science · 2020 · 193 citations

  • Computer Science
  • Biology
  • Computational biology

  ) is the most widely studied nonhuman primate (NHP) in biomedical research. We present an updated reference genome assembly (Mmul_10, contig N50 = 46 Mbp) that increases the sequence contiguity 120-fold and annotate it using 6.5 million full-length transcripts, thus improving our understanding of gene content, isoform diversity, and repeat organization. With the improved assembly of segmental duplications, we discovered new lineage-specific genes and expanded gene families that are potentially informative in studies of evolution and disease susceptibility. Whole-genome sequencing (WGS) data from 853 rhesus macaques identified 85.7 million single-nucleotide variants (SNVs) and 10.5 million indel variants, including potentially damaging variants in genes associated with human autism and developmental delay, providing a framework for developing noninvasive NHP models of human disease.

  DOI

Frequent coauthors

• Daniel T. Chang

  800 shared

• J. Costello

  Centre National de la Recherche Scientifique

  600 shared

• Olivier Gevaert

  600 shared

• Lih‐Shen Chin

  Shanghai University of Traditional Chinese Medicine

  600 shared

• Gary K. Steinberg

  Stanford Medicine

  600 shared

• Robert H. Bell

  Durham University

  600 shared

• Tali Mazor

  University of California, San Francisco

  600 shared

• Erik P. Sulman

  New York University

  600 shared

Labs

• Haussler-Salama LabPI

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